AI/ML Glossary
Core LLM Concepts
A-F
| Term | Definition |
|---|---|
| Alignment | Training AI to be helpful, harmless, and honest |
| Attention | Mechanism for tokens to consider other tokens |
| BPE | Byte Pair Encoding, a tokenization algorithm |
| Chain-of-Thought | Prompting technique for step-by-step reasoning |
| Context Window | Maximum tokens an LLM can process at once |
| Decoder | Transformer block that generates output tokens |
| DPO | Direct Preference Optimization, simpler RLHF alternative |
| Embedding | Dense vector representation of text |
| Encoder | Transformer block that processes input |
| Fine-tuning | Adapting a pretrained model to new data |
| Few-shot | Providing examples in the prompt |
G-L
| Term | Definition |
|---|---|
| Ground Truth | Correct labels for training or evaluation |
| Hallucination | LLM generating plausible but false information |
| Inference | Running a trained model to get predictions |
| Jailbreak | Circumventing LLM safety measures |
| KV Cache | Stored key-value pairs for efficient generation |
| LoRA | Low-Rank Adaptation, parameter-efficient fine-tuning |
| LLM | Large Language Model |
| Loss | Measure of prediction error during training |
M-R
| Term | Definition |
|---|---|
| MoE | Mixture of Experts architecture |
| Multimodal | Processing multiple data types (text, image, audio) |
| Perplexity | Exponential of cross-entropy, measures uncertainty |
| Prefix Caching | Reusing cached KV for common prefixes |
| Prompt | Input text given to an LLM |
| Quantization | Reducing numeric precision (FP16 → INT4) |
| RAG | Retrieval-Augmented Generation |
| RLHF | Reinforcement Learning from Human Feedback |
| RoPE | Rotary Position Embedding |
S-Z
| Term | Definition |
|---|---|
| SFT | Supervised Fine-Tuning on instruction data |
| Speculative Decoding | Using draft model to accelerate generation |
| System Prompt | Instructions defining AI behavior |
| Temperature | Controls randomness in generation |
| Token | Subword unit processed by LLM |
| Top-p | Nucleus sampling parameter |
| Transformer | Neural network architecture with attention |
| TTFT | Time to First Token |
| VLM | Vision-Language Model |
| Zero-shot | Prompting without examples |
Infrastructure Terms
| Term | Definition |
|---|---|
| CUDA | NVIDIA's GPU computing platform |
| Flash Attention | Memory-efficient attention algorithm |
| HBM | High Bandwidth Memory (GPU memory) |
| NVLink | High-speed GPU interconnect |
| TensorRT | NVIDIA inference optimization library |
| vLLM | High-throughput LLM serving engine |
| GGUF | File format for quantized models |
Metrics
| Term | Definition |
|---|---|
| BLEU | Machine translation quality metric |
| F1 | Harmonic mean of precision and recall |
| Pass@k | Code generation success probability |
| TPOT | Time Per Output Token |
| WER | Word Error Rate for speech recognition |
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